The project will use the manufacturing environment as a paradigm for modeling and studying problems in distributed and hierarchical control under uncertainty. The primary objective of this project is to investigate and propose solutions to computational challenges that are the result of the proliferation of technologies which give rise to stochastic large-scale systems of unprecedented complexity. The second objective is to develop solutions and methodologies that not only meet the needs of the manufacturing supply chain coordination problem, but are general enough to be able to explore their impact on other areas of science and technology.
The manufacturing enterprise is one consisting of multiple, but highly interconnected, hierarchical layers. The size, nonlinearity, and stochasticity of manufacturing systems renders effective, let alone optimal, centralized decision making intractable. The approach followed in the past has been to relate the system's state to a number of variables, e.g., randomness, decision vector, cost, and then solve for decoupled components in a decentralized decision framework. This produces tractable, but inefficient, decision rules because of the assumptions made to allow solutions.
The goal of this research is to achieve systematic coordination of decentralized decision entities without compromising tractability of the problem. The issues involved span methodologies ranging from Stochastic Control and Large Deviations Theory, to Discrete Event Dynamic Analysis, Neuro-dynamic Programming, and Concurrent Simulation.
Since many of the techniques to be developed involve optimization and stochastic systems, the relevance of the methodological research on economics and statistical physics will be also examined.
Strong ties with industry will allow deployment, implementation, and testing in an actual manufacturing environment to be included as part of the dissemination and transfer plan for the research approaches developed.
